JAQM Volume 12, Issue 2 - June 30, 2017

Contents

A two-parameter weighted Shanker distribution which includes one parameter Shanker distribution introduced by Shanker (2015) as a particular case has been proposed for modeling lifetime data. Structural properties of the proposed distribution including moments and moment related measures, hazard rate function, mean residual life function and stochastic orderings have been discussed. The estimation of its parameters has been discussed using maximum likelihood estimation and statistical inferences based on estimated values of parameters have been presented. The goodness of fit of the proposed distribution have been discussed with two real lifetime data sets and the fit has been compared with one parameter exponential, Lindley and Shanker distributions, and two-parameter weighted Lindley distribution and the fit in all data sets is found to be quite satisfactory.

During last decade, both scientists and companies began to create increasingly more models in order to analyze and predict consumer behavior. The goal of this paper is to identify the effects of offers on the purchase decision process (is the buying behavior changing according to the offers and do consumers buy products only according to their needs). For that a questionnaire was filled in by supermarket consumers. According to their answers, most of the consumers will not go to a specific supermarket driven by the offers they can find there (location and the products are more important factors), but they may pick a different product because of that. In conclusion, offers may change the buying behavior once the consumers are inside the supermarket, ultimately making them to choose products that they may not really need.

Composite Index (CI) depends on method of combining several variables or indicators to reflect overall assessment. Each method of combining the component indicators results in different values of CI and different rankings from a given dataset.
The paper describes problems for construction of CI at various stages and proposes a number of methods for obtaining CI along with desired properties which a good CI will satisfy. Existing and proposed methods to construct CI can be compared with respect to those desired properties. The Geometric Mean approach satisfies all the desired properties and avoids calculation of weights or variance-covariance matrix or correlation matrix. The Geometric Mean approach is applicable for situation even where only the two vectors X and T are given for the current year and the previous year. Thus, the Geometric Mean approach is well applicable for assessment of impact. The approach also helps to identify relative importance of the component indicators in terms of values of the ratios and also identify the critical areas and facilitate initiation of corrective measures. Such identification is important from a policy point of view. The GM method reduces the level of substitutability between component indicators and facilitates statistical test of significance of equality of two geometric means. Thus, the Geometric Mean approach and may be taken as best among the methods discussed.

One of the most widely used methods in assessing the efficiency of public policies and programs for a set of units is Data Envelopment Analysis (DEA). DEA is a non-parametric method which identifies an efficiency frontier on which only the efficient Decision Making Units (DMUs) are placed, by using linear programming techniques. By applying non-parametric techniques of frontier estimation, the efficiency of a DMU can be measured by comparing it with an identified efficiency frontier. In this paper we have used DEA for evaluating the efficiency of the European structural funds allocated to finance the educational infrastructure through the Regional Operational Program 2007-2013, implemented in Romania. The output variables measure the educational performance as well as the school drop-out rate, while the focal input variable is the value of European funds. Romanian counties are considered to be the decision making units (DMUs) and our results confirm the deep disparities existing between Romanian counties concerning the efficient use of European structural funds.